Cloud Particles Evolution Algorithm

Many evolutionary algorithms have been paid attention to by the researchers and have been applied to solve optimization problems. This paper presents a new optimization method called cloud particles evolution algorithm (CPEA) to solve optimization problems based on cloud formation process and phase transformation of natural substance. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The cloud is composed of descript and independent particles in this algorithm. The cloud particles use phase transformation of three states to realize the global exploration and the local exploitation in the optimization process. Moreover, the cloud particles not only realize the survival of the fittest through competition mechanism but also ensure the diversity of the cloud particles by reciprocity mechanism. The effectiveness of the algorithm is validated upon different benchmark problems. The proposed algorithm is compared with a number of other well-known optimization algorithms, and the experimental results show that cloud particles evolution algorithm has a higher efficiency than some other algorithms

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Dynamical System Methods in Fluid Dynamics

The workshop was organized around the infusion of new techniques from dynamical systems, geometric methods, multiscale analysis, scientific computation, and control theory into traditional methods in fluid mechanics. It was well attended with about 45 participants with broad geographic representation from all continents. There was an excellent blend of senior researchers, students, postdocs and junior faculty

Automatic discovery of complex causality

This study entails the understanding of and the development of a computational method for automatically extracting complex expressions in language that correspond to event to event sequential relations in the real world. We here develop component procedures of a system that would be capable of taking raw linguistic input (such as those from narrative writings or social network data), and find real-world semantic relations among events. Such an endeavor is applicable to many types of sequential relations, for which we use causality as a case study, both for its importance as a prominent type of sequential relation between events, as well as for its general prevalence in natural language. But we also demonstrate that the idea is also applicable in principle to other major types of event to event relations, such as reciprocity. The study primarily focuses on those types of causalities that contain complex structures and require in-depth linguistic analyses to discover and extract. Designing an automated method for the extraction of structurally complex causal expressions entails methodologies and theories that are beyond conventional methods used in computational semantics. The classes of adjunctive causal structure, and embedded causal structure are types that are hard to access using traditional methods, but more amenable for methods developed in this study. The principal procedures employed for the extraction of these are a heavily mod- ified form of Hidden Markov Model (HMM), which we use to deal with causal structures that have sequentially complex makeup. We also designed a highly modified Genetic Algo- rithm (GA) adapted for embedded context-free structures, used to rank and extract those causal structures that have deep embedding at the syntax-semantics interface. These will be reformulated, augmented, and explored in depth. With these methods using unsupervised and semi-supervised learning, we were able to obtain reasonable results in terms of discrimination of causal pairs ⟨ei,ej⟩ pairs and some longer chains of causation from corpora. From these results, we were also able to perform additional linguistic analysis over their theoretical semantic structure, and observe aspects of each that allows us to sub-classify the relations according to standard ideas in formal logic as well as from behavioral psychology. These methods would be critical to a system for building a graph theoretic representation of a social network, from corpora produced by entities within that network, which would utilize the methods described in this project, and similar approaches can be extended to model and discover other types of complex event- relations. These types of fundamental technologies, would in turn, help us to design and build the types of on-line and mobile services that provide increased machine awareness of user behavior and to be able to target and cater to users individually

Aeronautical engineering: A continuing bibliography, supplement 122

This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)

Maternal gene evolution from a comparative perspective

Foreldreomsorg er et utbredt fenomen i livets tre. Arter bruker ulike midler på å ta vare på avkommet sitt. En metode er å tilføre proviant til de ubefruktede eggene. Slike tilførte midler dekker kravene til energi gjennom første del av utviklingen og kan til og med orkestrere utviklingen. Regulering oppnås ved å tilføre og lagre transkripsjonsfaktorer og proteiner fra mor-organismen, i eggene. Disse faktorene blir etter en stund byttet med sine zygotiske motstykker, i en prosess som kalles den maternelle til zygotiske overgangen. Tidligere forskning har avdekket mange spennende mekanismer som ligger til grunn for den maternelle til zygotiske overgangen. I velstuderte modellarter er de spesifikke faktorene, samt deres romlige og tidsmessige dynamikk godt kjent. Men det gjenstår fortsatt kunnskapshull i mindre studerte arter. Ved å fylle disse hullene får man en bedre forståelse av de evolusjonære prosessene som har formet denne gruppen gener. Den biologiske betydningen av maternelle gener både fra et utviklingsperspektiv og et sykdomsperspektiv, rettferdiggjør en grundig analyse av evolusjonen deres. I oppgaven min er målet å bedre forståelsen av maternelle gener ved å identifisere og beskrive gen-repertoaret i virvelløse dyr der dette ikke har vært undersøkt før. Videre, ved hjelp av et komparativt rammeverk, gir jeg et innblikk i den molekylære utviklingen av maternelle gener. Resultatene tyder på at disse genene, til tross for store endringer på sekvensnivå, viser en bevaring av ekspresjonsprofilene.Parental care is a widespread phenomenon across the tree of life. Species use various means of caring for their progeny. One such care is through provision supplied with the unfertilized eggs. Such provisions are sufficient to cover the energetic requirements of early development or even orchestrate them. Regulating said events is achieved through the supply of transcripts and proteins synthesized by the maternal organism and stored in the eggs. These regulators are then exchanged to their zygotic counterparts when transcription is resumed during development in a process known as the maternal to zygotic transition. Past research efforts had uncovered many intriguing mechanisms underlying this transition. In commonly employed model species the specific factors, their spatial and temporal dynamics are well known. Nevertheless, a gap still remains in lesser studied species. By filling in such gaps a better understanding of the evolutionary processes shaping this group of genes can be achieved. Their biological importance, both from a developmental perspective and from a disease perspective, warrants an in-depth analysis of their evolution. In my thesis my aim is to contribute to a better understanding of the evolution of maternal genes by identifying and describing the maternal gene repertoire of lesser known invertebrate species. Furthermore, by utilizing a comparative framework I provide an insight into the molecular evolution of maternal genes. My results suggests that maternal genes, despite being characterized with high sequence divergences, show a conservation in their expression profile.Doktorgradsavhandlin

Scientific Advances in STEM

Following a previous topic (Scientific advances in STEM: from professors to students; https://www.mdpi.com/topics/advances_stem), this new topic aims to highlight the importance of establishing collaborations among research groups from different disciplines, combining the scientific knowledge from basic to applied research as well as taking advantage of different research facilities. Fundamental science helps us to understand phenomenological basics, while applied science focuses on products and technology developments, highlighting the need to perform a transference of knowledge to society and the industrial sector